Monthly Archives: December 2014

December 25, 2014 marks 40 years since Cyclone Tracy made landfall early Christmas Day over the coast of Australia, devastating the Northern Territory city of Darwin. As the landfall anniversary approaches, we remember one of the most destructive storms to impact Australia and are reminded of the time when “Santa Never Made it into Darwin.”

Small and intense, Tracy’s recorded winds reached 217 km/hr (134 mph), a strong category 3 on the 5-point Australian Bureau of Meteorology scale, before the anemometer at Darwin city airport failed at 3:10 am, a full 50 minutes before the storm’s eye passed overhead. Satellite and damage observations suggest that Tracy’s gust winds may have topped 250 km/hr (155 mph) and the storm’s strength is generally described as a category 4. At the time, it was the smallest tropical cyclone ever recorded in either hemisphere, with gale force winds at 125 km/hr (77 mph) extending just 50 km (31 mi) from the center and an eye only about 12 km (7.5 mi) wide when it passed over Darwin. (Tracy remained the smallest tropical cyclone until 2008 when Tropical Storm Marco recorded gale force winds that extended out to only 19km (12 mi) over the northwestern Caribbean).

Although small, Cyclone Tracy passed directly over Darwin and did so while tracking very slowly—causing immense devastation, primarily wind damage and predominantly residential structural damage. Around 60 percent of the residential property was destroyed and more than 30 percent was severely damaged. Only 6 percent of Darwin’s residential property survived with anything less than minor damage. Darwin had expanded rapidly since the 1950s, but throughout that time structural engineering design codes were typically not applied to residential structures.

The insurance payout for Tracy was, at the time, the largest in Australian history at 200 million (1974) Australian dollars (AUD), normalized to 4 billion (2011) AUD, according to the Insurance Council of Australia. It has been surpassed only by the payout from the 1999 Sydney Hailstorm at 4.3 billion (2011) AUD.

The RMS retrospective report that was released around the 30th anniversary of the storm provides information on the meteorology of the cyclone and the wind damage. The report also highlights the impact on wind engineering building codes (particularly residential) that were introduced as a result of the cyclone during reconstruction in Darwin and in cyclone affected regions of Australia—resulting in some of the most stringent building codes in cyclone-exposed areas across the world.

Darwin was completely rebuilt to very high standards and relatively new, structurally sound buildings now dominate the landscape. Most certainly, Darwin is better prepared for when the next cyclone strikes. However, the building stock in other cyclone-exposed cities of Australia is mixed. Most coastal cities are a blend of old, weak buildings and newer, stronger buildings, which are expected to perform far better under cyclone wind loading. The benefits of improvements in both design code specifications and design code enforcement have been demonstrated in Queensland by Cyclones Larry (2006) and Yasi (2011). Most of the damage to residential buildings in those storms was suffered by houses constructed before 1980, while those built to modern codes, incorporating the lessons learned from Cyclone Tracy, suffered far less damage. While progress has clearly been made, it is sobering to remember there are many more pre-1980 houses remaining in cyclone-prone areas of Australia.

Australia Cyclone season runs from November to April. The 2014/2015 season is forecast to be an average to below average season in terms of tropical cyclone activity off Australia waters, according to the Australian Government Bureau of Meteorology.

Michael Drayton contributed to this post. Michael Drayton has been developing catastrophe models for RMS since 1996. While based in London, he worked on the first RMS European winter storm model and U.K. storm surge models, lead the development of the first RMS basin-wide Atlantic hurricane track model, and oversaw the hazard development work on the first RMS U.K. river flood model. Since moving back to New Zealand in 2004, Michael has updated the RMS Australia cyclone hazard model and led the development of the RMS Australia (Sydney) severe convective storm model. He works on U.K. storm surge updates and supports U.S. hurricane model activities including audits by the Florida Commission on Hurricane Loss Projection Methodology. Ever since the 2011 Christchurch earthquake, Michael has been increasingly involved with the local insurance market and research communities. He received a BS degree in civil engineering, with honors, from the University of Canterbury and a PhD in applied mathematics from King’s College, Cambridge.

This December marks the 10-year anniversary of the Indian Ocean earthquake and tsunami, a disaster that killed more than 230,000 people in 14 countries. The disaster hit Thailand and Indonesia especially hard and is considered one of the ten worst earthquakes in recorded history based on damages.

In advance of the anniversary on December 26, 2014, Dr. Robert Muir-Wood, RMS chief research officer, and Dr. Patricia Grossi, RMS senior director of global earthquake modeling, hosted their second Reddit Science AMA (Ask Me Anything). Back in October, Muir-Wood and Grossi hosted another AMA on the 25th anniversary of the Loma Prieta earthquake in the San Francisco Bay Area.

The latest Reddit thread generated almost 300 comments. Muir-Wood and Grossi discussed topics including: early warning systems for disasters like tsunamis, what variables are considered in catastrophe models, and if better building design can protect against natural disasters – particularly tsunamis. Highlights of the chat follow:

What kind of structural elements or configurations are best to combat or defend against these disasters?

Muir-Wood: There have been research studies on buildings best able to survive tsunamis. The key is to make them strong (from well engineered reinforced concrete) but with ground floor walls running parallel with the shoreline that are weak, so that the walls can be overwhelmed without threatening the whole building.

The 2004 Indian Ocean tsunami took a lot people by surprise due to the lack of a tsunami warning system even though there was a gap between the earthquake and the tsunami. If there was a tsunami warning system in place at the time would that have decreased the death toll by a lot, or not make too much of a difference considering how strong the tsunami was.

Grossi: Early warning systems are excellent tools for reducing the loss of life during an earthquake-induced tsunami event. But education is one of the easiest ways to reduce tsunami life loss. Such education needs to include knowledge of the cause of a tsunami and its association with the largest earthquakes to help individuals understand how their own observations can help them take appropriate action (e.g., seeing the water recede from the coastline). In essence, official warning systems can provide only part of the solution, as information can never be effectively disseminated to everyone along a coastline. With only 10 to 30 minutes warning in the nearfield of major tsunamis, it is imperative that people are taught to take their own action rather than wait for official instruction.

Show me the coolest tsunami video.

Muir-Wood: There are amazing videos of the Japan 2011 tsunami. I wouldn’t pick just one of them – but recommend you watch quite a few – because they are interestingly different. The most amazing feature of the tsunami is the way the water can continue to rise and rise, for five or ten minutes, apparently without end. And then how the people watching the tsunami, climb to higher locations and then realize that if it keeps rising there will be nowhere for them to go.

Was there anything we missed you wanted to discuss? Please let us know in the comments.

Acts of terror can result in wide ranges of potential damage and the financial repercussions can threaten an insurer’s solvency.

Terrorism risk can be modeled probabilistically with an increasing degree of confidence. Its damages at long return periods are comparable to natural disasters such as hurricanes and earthquakes.

The events of September 11, 2001 resulted in insurable damages in excess of $44 billion, causing insurers to explicitly exclude terrorism from standard property policies. This resulted in the downgrade of billions in mortgage securities, and the costly delay of many important development, construction, and infrastructure projects.

The Terrorism Risk Insurance Act (TRIA)

To address the terrorism insurance shortage, the Terrorism Risk Insurance Act (TRIA) was signed into law by President George W. Bush in 2002, creating a $100 billion federal backstop for insurance claims related to acts of terrorism.

Originally set to expire December 31, 2005, it was extended for two years in December 2005, and again in 2007. The current extension, entitled the Terrorism Risk Insurance Program Reauthorization Act (TRIPRA), will expire on December 31, 2014 and its renewal is up for debate in Congress.

Insuring Against Terrorism

Just as with natural catastrophe risk, insurers rely on catastrophe models to underwrite and price terrorism risk.

Terrorism threat is a function of intent, capabilities, and counter-terrorism action; counter-terrorism factors have an impact on frequency, multiplicity, attack type, and targeting of terrorist actions, as well mitigation of loss. It’s not just what the terrorists can do that controls the outcome; it’s what governments can do to counteract their efforts.

RMS was first-to-market with a probabilistic terrorism model and has been providing solutions to model and manage terrorism risk since 2002. The RMS Probabilistic Terrorism Model takes a quantitative view of risk, meaning it uses mathematical methods from game theory, operational research, and social network analysis to inform its view of frequency. Its development involved the input of an extensive team of highly qualified advisors, all of which are authorities in their field of assessing terrorism threats.

The Probabilistic Terrorism Model is made up of four components:

The potential targets (comprised of landmark properties in major cities) and associated attack mode combinations (both conventional and CBRN – chemical biological, radiological, and nuclear), knowing that not every target is susceptible to all types of attack modes.

The relative likelihood of an attack, taking into account the target and type of attack. For example, attacks using conventional bombs are easier to plan for and execute than anthrax releases; locations having high symbolic or economic importance are much more likely to be targeted.

The relative likelihood of multiple attacks making up a single event. For example, a hallmark of many terrorist operations is to attack two or more targets simultaneously. Attack multiplicity modeling is derived based on terrorist groups’ ability to coordinate multiple attacks for a particular weapon type.

Event frequency, which is empirically-driven and determined by modeling three input parameters: the number of attempted events in a year, the distribution of success rate of attempted events, and a suppression factor that is based on government response to an event.

The RMS terrorism model’s damage module has been validated against historical terrorism events. All known terrorist plots or attacks that have occurred since the model’s launch have been consistent with our underlying modeling principles. There are blue ocean opportunities for those willing to understand terrorism risk and underwrite it accordingly.

This isn’t the first time Sony has experienced a virtual assault. In 2011, the PlayStation Network suffered one of the biggest security breaches in recent memory, which is reported to have cost the company in excess of $171 million.

Cyber attacks can be costly and insurers are hesitant to offer commercial cyber attack coverage because the risk is not well understood.

Andrew and his co-authors contend that insurers are not concerned with individual loss events, such as the targeted security penetrations we’ve seen recently on Sony and JP Morgan. It’s whether individual loss events are manageable across a whole portfolio of policies.

The biggest challenge in evaluating cyber risk is its inherent systemic complexity and interconnectivity. The internet, the technology companies that run on it, and the enterprises they serve are inextricably intertwined; shocks to one part of a network can quickly cascade and affect the rest of the whole system.

Can catastrophe-modelling methodologies provide the solution? Read the full article in The Actuary here.

California is bracing for a major storm this week. Many schools are closed and residents are hunkering down in preparation for potential flooding. Not to be outdone by the East Coast, which has come up with monikers like “snowmageddon” and “snowpocalypse” for their recent storms, some are referring to it as the “hellastorm.”

Estimates this morning are predicting 1 to 5 inches of rain from Northern California up to Washington, 1 to 2 feet of snow in the Sierra Nevada mountains, and wind gusts over 50 miles per hour in the interior regions.

It will happen again.

Storms like these are not uncommon, occurring once every 5 to 10 years. So we could experience another one before the end of the decade.

The drought is partially to blame.

While drought conditions are not a necessity for these types of events, they can increase the impact of flooding because the ground cannot absorb water fast enough. The same can occur when the sustained heavy rain falls on ground is already saturated.

The current rain came all the way from Hawaii.

Storms like this are dependent on many variables. In this case, the excessive rain and snowfall is being driven by the position of the jet stream and what’s known as the Pineapple Express, an atmospheric plume of tropical moisture that flows from the sub-tropics near Hawaii to the U.S. West Coast. It generally occurs during El Nino years, but in this case, forecast El Nino conditions did not fully develop. In other words, it’s a weak one.

UPDATE: Northern California has gotten more than 8 inches of precipitation so far. Sustained winds were forecast to be up to hurricane force (70 to 80 mph) in the local mountains and up to 100 mph in the higher elevations across the Sierra summit. A wind gust to 147 mph was recorded at high altitude peak near Lake Tahoe, that had surfers catching 7-foot waves on the lake!

I recently presented at a two-day seminar organized by the Geneva Association in collaboration with the XL Group with a key theme of “urban risk”—a pressing topic, as more than 50 percent of the world’s population currently lives in cities. By 2030, the proportion will have risen to 60 percent.

1) Desire to be close to the center of the city inevitably raises land values, and encourages land to be “reclaimed” for development, while still remaining susceptible to flooding, liquefaction, and amplified earthquake shaking.

3) Land in the city that is abandoned as too landslide-prone or too flood-prone gets built on anyway by informal settlements, which then add to the urban risk.

4) The September 11, 2001 attacks and the 1985 Mexico City earthquake highlighted situations where people living or working in high-rise city center buildings were at much greater risk than those beyond the city or in the suburbs.

6) When the urban flood defenses do break, such as New Orleans in 2005 or in Nagoya, Japan in 2000, the consequences are much worse than if the defenses had not been constructed, because some cities have been developed as if there is no risk.

8) Cities are totally dependent on electrical power. When the power goes out, all kinds of functions stop. For example, during Superstorm Sandy, many disabled people were stranded in their apartment buildings in the absence of working elevators.

9) Cities can show reduced levels of social support in disasters, as seen in the higher casualty rates in Paris during the August 2003 French heat wave.

10) Lastly, cities are typically located on a waterfront where they inevitably confront frontline water hazards such as storm surge or tsunami.

So, does that all add up to higher levels of risk?

To answer that question, we would need to perform a controlled study of two regions: one with a big city and one without, to model the comparative loss ratio for people and property across all perils. Factors such as economic wealth levels would need to be taken into consideration.

What we know for sure is the risks of a big city are different to those of rural areas. Going forward, we will inevitably see an increasing proportion of big urban disasters like those of the last decade in New Orleans, Port au Prince, Christchurch, and New York.

Only thirteen months since Super Typhoon Haiyan devastated parts of the Philippines, the region is again under threat from a large typhoon. Typhoon Hagupit, locally referred to as Typhoon Ruby, is currently Category 4 strength and expected to make landfall over the weekend.

Hagupit isn’t forecast to be as strong as Typhoon Haiyan; however, its projected path takes it across southern Luzon Island, as well as an area 35 miles south of the Central Capitol Region that has a high concentration of exposure at risk of substantial wind damage.

As the typhoon makes landfall, there is also the potential for storm surge along low-lying coastal areas, which are characterized by complex coastlines and bays.

At this stage,there still remains a large degree of uncertainty surrounding Hagupit’s forecast track, intensity, and landfall locations, which the RMS catastrophe response team is monitoring closely.

High concentrations of exposure at risk

The Central Capitol Region includes Quezon City, the largest city in the Philippines, as well as Manila, which is the second largest city and serves as the capitol. This region has the highest concentration of economic insurable exposure ($165.5 billion), which accounts for approximately 20 percent of the country’s total insurable exposure. Using the RMS Economic Exposure datasets, we can see that $91 billion is residential, $59 billion commercial, and $14.4 billion is industrial exposure. Quezon City has the highest value of insurable exposure with $32.3 billion, of which 65 percent is commercial.

In addition, our Industrial Clusters Catalog shows that a high proportion of industrial clusters could be impacted by Hagupit. These are located in the surrounding districts of Rizal and Laguna within the Calabarzon region, as well as the Lima Technology Center, which is in the direct path of typhoon.

Despite being forecast to making landfall further north than Typhoon Haiyan, Hagupit is still likely to affect areas that are still recovering from the impacts of Typhoon Haiyan.

Lessons from Typhoon Haiyan Reconnaissance

Typhoon Haiyan illustrated that the complex geometry and shallow water where Haiyan made landfall can give rise to significant storm surge heights, evidenced by high surge levels experienced in San Pedro and San Pablo Bays, affecting Tacloban City. The Philippines is characterized by these complex coastlines and shallow waters, and future typhoon events, including Typhoon Hagupit, could similarly cause significant storm surge in other areas of the Philippines.

In the aftermath of Typhoon Haiyan, our scientists conducted extensive field reconnaissance work. They observed that buildings were structurally more resilient to typhoon winds because of the region’s high risk of earthquakes. There is abundant use of reinforced concrete frames, which ensures the structural integrity of the buildings for earthquakes and winds.

Wind alone did not cause substantial structural damage to structures built with reinforced concrete; however, the severe storm surge flooding caused the failure of some reinforced concrete framed buildings. Instead, wind damage was most evident to the roofing of buildings, particularly light aluminum roofs. Large span commercial and industrial light metal roofs collapsed, but concrete roof tiles preformed better.

Since Haiyan, the Filipino government has been actively discussing sponsoring a catastrophe bond with the World Bank, but the process is complex and will take time to develop.

On Sunday, December 26, 2004 at approximately 8 a.m. local time, a massive earthquake occurred along the Indian–Burma plate boundary off the coast of Sumatra, Indonesia. Rupturing over 1,200 km of the Sunda Trench, the magnitude of the earthquake has been estimated between M9.0 and M9.3—with the U.S. Geological Survey’s Centennial Earthquake Catalog estimating M9.1. Occurring at a fairly shallow depth—less than 30 km—the earthquake generated a basin-wide tsunami that inundated coastlines across the Indian Ocean and caused run-up waves farther afield, impacting the eastern coastline of Africa. By the end of the day, it was apparent that the event was going to emerge as one of the worst natural disasters in modern times.

Overall economic losses from the 2004 disaster were approximately $10 billion, with the majority of loss attributed to the damage in the Indonesia, Thailand, Sri Lanka, and India. The large majority of property damage was caused by the tsunami waves. Along coastlines of most of the affected countries, buildings were situated closer to sea level than is typical of higher latitudes, exacerbating the impacts.

In the aftermath of the event, the international relief efforts across the Indian Ocean were seen as fairly effective. But the longer-term recovery work in certain regions has struggled—due to the overwhelming numbers of people displaced from their homes. There are, of course, examples of well-executed reconstruction efforts. Build Change—a partner organization of RMS—has worked with tsunami survivors in Banda Aceh, Sumatra to rebuild safe, sustainable homes. Ten years after the event, evidence of the destruction wrought by the tsunami remains in the high-impacted areas.

Humanitarian Impact

While tsunami in the Indian Ocean have certainly occurred many times before, from the perspective of modern history, the human casualties from the 2004 Indian Ocean Earthquake and Tsunami have no historical equal. More than 225,000 people lost their lives in the disaster, with most of the loss of life occurring in the near field in Sumatra, Indonesia. In Indonesia, the tsunami destroyed virtually every village, town, road, and bridge along a 170-km stretch of coast less than 10 m above sea level. Sri Lanka’s Eastern and Southern provinces were severely impacted, with fatality rate among the population within 1 km of coast between 15% and 20%. In India, entire villages in Tamil Nadu were destroyed.

In Thailand, the tsunami affected local inhabitants and foreign tourists in the densely inhabited Phuket Island. The fatalities among the tourists were a significant proportion of the overall loss of life, as many were on the beach or in hotels near the sea at the time the tsunami waves struck. In addition, the initial tsunami wave in Phuket, which was east of the rupture, began with a receding wave. Many of the tourists (not indigenous to tsunami-prone coastal regions) were unfortunately not familiar with the nature of tsunami waves. In many (but not all) tsunami, the first movement of the sea is a withdrawal. Any occasion when the sea level recedes rapidly and inexplicably should be taken as a signal for immediate flight to higher ground.

Managing Tsunami Risk in the Aftermath

The 2004 Indian Ocean Tsunami highlighted inherent vulnerabilities in the world’s coastlines and the people who live there. Coastal populations are on the increase in many parts of the world, mostly due to the exploitation of sea resources or tourism-related activities. Adequate tsunami mitigation measures— such as tsunami warning systems, education, and land use planning—can be put in place to save lives, property, and the livelihoods of those living on the coast.

Although the impact of the 2004 disaster on the global insurance industry was minimal, it alerted the world to the dangers of tsunami hazards. Worldwide response to the 2004 disaster resulted in the establishment of the Indian Ocean Tsunami Warning and Mitigation System in 2006.

Chennai, India

9:30 a.m. local time

On the Indian peninsula, the hardest-hit areas were on India’s southeastern coast, in the state of Tamil Nadu, where close to 8,000 perished. Chennai, the capital of Tamil Nadu, has rebounded to become one of the Rockefeller Foundations’ “100 Resilient Cities” for its commitment to minimizing the impact of flooding in low-lying coastal areas and adopting a tsunami early warning system.

Just north of the earthquake’s epicenter, India’s Andaman and Nicobar islands were struck by waves reaching 4 to 15 m (13 to 50 ft) within 10 minutes of the earthquake. The death toll reached 7,000, with many more missing and presumed dead.

Distance from Epicenter

2,020 km

(1,260 mi)

Wave Height

5 m

(16 ft)

Time from initial rupture

3 hours

Banda Aceh, Indonesia

8:30 a.m. local time

The first wave reached Sumatra, Indonesia’s largest island, approximately 30 minutes after the initial rupture. Banda Aceh, the area hardest hit by the tsunami and closest major city to the earthquake’s epicenter, sustained more than 31,000 casualties in the city alone. Entire towns in the surrounding areas, some with populations of more than 10,000, vanished. More than 600,000 people in Aceh’s fishery and agricultural sectors lost their livelihoods.

Four times more women than men were killed—not just in Indonesia, but India and Sri Lanka as well—as many men were fishing, while women were on the beach, waiting for the fishermen to return, or at home, minding their children.

Distance from Epicenter

260 km

(160 mi)

Wave Height

30 m

(100 ft)

Time from initial rupture

30 min

Patong Beach, Thailand

9:30 a.m. local time

Tourism is one of Thailand’s key economic sectors, comprising about 12% of its overall GDP, with the greatest economic development along Thailand’s western coast. Khao Lak, Ko Phi Phi, and Phuket, with their pristine beaches, placid waters, and coral reefs, are among some of the most visited places on Earth. They were also the areas hit hardest by the tsunami.

The earthquake struck during the height of Thailand’s tourist season, causing close to 5,400 confirmed deaths, with many thousands more missing and presumed dead.

Distance from Epicenter

580 km

(360 mi)

Wave Height

6 m

(20 ft)

Time from initial rupture

1.5 hours

Galle Port, Sri Lanka

10:00 a.m. local time

Before the tsunami hit, elephants were observed running away from Patanangala beach in Yala National Park, directly in the tsunami’s path. Flamingos, goats, and buffaloes also moved to higher ground. All but two water buffaloes were unharmed.

When the waves came, Sri Lanka’s eastern and southern provinces were the hardest hit. In the coastal town of Telwatta, the tsunami struck an overcrowded train packed with passengers for the Buddhist full moon and Christmas holiday weekend. More than 1,700 lives were lost in what became the worst humanitarian disaster in railroad history.

Distance from Epicenter

1,750 km

(1,100 mi)

Wave Height

6 m

(20 ft)

Time from initial rupture

3 hours

Wave Height

6 m

(20 ft)

Northern Sumatra, West Coast

7:58 a.m. local time

The M9.1 earthquake struck 160 km (100 mi) off the northwest coast of Sumatra, Indonesia, generating the deadliest tsunami in history. With a rupture length of more than 1,200 km (750 mi), the earthquake released energy equivalent to 475 megatons of TNT, and shot a massive water column into the air.

The water settled back into the open ocean as a barely perceptible swell of only 50 cm (1.6 feet)—but moved at speeds of more than 600 km/hr (370 mph). It slowed toward the coast, inundating Sumatra with waves of up to 30 m (100 feet), leaving more than 225,000 people missing or presumed dead, and displacing 1.5 million more.

While wind speed can indicate a storm’s damageability, two storms with similar peak wind speeds can cause vastly different levels of damage if they pass over locations with different concentrations of exposure.

This month marks the 15th anniversary of Lothar and Martin. Two powerful storms that tracked violently across Europe on December 26-28, 1999.

The combined European loss of both storms is in excess of $11 billion (2013 values). Since the storms occurred within days of each other it’s difficult to calculate the exact split of damage, however a 70:30 ratio is commonly accepted, ranking Lothar as the second largest Europe windstorm loss on record after Daria (1990).

France was hit hardest by the storms—particularly Paris, which was right in the bullseye of Lothar’s most extreme physical characteristics. The recorded wind speeds in the low-lying regions of Paris were above 160 km/h and as high as 200 km/h at the top of the Eiffel Tower.

An exceptional storm

While Lothar’s wind speeds are comparable to other historical Europe windstorms, it’s considered an exceptional event for the insurance industry because of its track and the timing of its maximum intensification over Paris. Today, Lothar is a key benchmark used by the industry to understand the potential magnitude of Europe windstorm losses.

Lothar – a one-off for France?

Many industry experts believe Lothar to be higher than a 100-year return period loss event for France; however this should be interpreted as a long-term average and France could potentially experience a similarly extreme storm this winter.

Using current industry exposures, RMS calculated the potential French losses that would result from a Lothar-like storm striking different locations in France. By relocating Lothar’s peak gusts along points up to 500 km in each direction from their original location, our modelers concluded that Lothar was the fourth worst-case storm that could have happened out of a total of 437 scenarios.

The worst-case scenario for France is a Lothar-like storm relocated approximately 100 km west of the original event but which would still significantly impact Paris. The losses from this scenario are not much higher than Lothar’s. At only 15 percent higher the small increase in loss reinforces Lothar as an exceptional benchmark for the insurance industry.

We found that the majority of scenarios in the study produced notably lower losses. This is because the displacement of the storm, by even small distances, meant that the most extreme wind speeds impacted much lower concentrations of insured exposures. The study reinforces our understanding of the sensitivity of windstorm loss to a storm’s path. It also highlights the importance of using a stochastic model containing tens of thousands of events to be able to comprehensively evaluate potential windstorm losses.

London at risk

No European city is immune from damaging windstorms. RMS also re-located Lothar over London—only a 350 km shift to the north—to see what the impacts would be. We calculated the insured loss for Europe could be as much as 25 percent higher than Lothar’s losses and potentially bigger than the $8.6 billion loss caused by Daria.

The uncertainty inherent to the climatic phenomena that drive windstorms makes it impossible to forecast exactly when and where the next strong storm will hit France or Europe. However, catastrophe models can at least help to evaluate the potential financial impact of extreme storms like Lothar.